Jamie K. Pringle

Bio: Jamie K. Pringle is an academic researcher from Keele University. The author has contributed to research in topics: Ground-penetrating radar & Forensic geophysics. The author has an hindex of 20, co-authored 98 publications receiving 1524 citations. Previous affiliations of Jamie K. Pringle include Heriot-Watt University & University of Liverpool.

Virtual outcrop models of petroleum reservoir analogues: a review of the current state-of-the-art

Abstract: A subsurface reservoir model is a computer based representation of petrophysical parameters such a porosity, permeability, fluid saturation, etc. Given that direct measurement of these parameters is limited to a few wells it is necessary to extrapolate their distribution. As geology is a first order control on petrophysics, it follows that an understanding of facies and their distribution is central to predicting reservoir quality and architecture. The majority of reservoir modelling systems used for the subsurface are based on correlation of seismically-derived surfaces to define reservoir zones. Well data are then used to define further, sub-seismic scale horizons and determine the zone properties which are represented in grid cells. Understanding the distribution of both sub-seismic surfaces and potential heterogeneous geology between them remains a significant challenge. Furthermore as the typical grid cell size is c. 50-200 m2 it is challenging to incorporate small-scale heterogeneities. It is critical, therefore, to use realistic values for both key stratigraphic horizons and internal facies distributions. Depositional facies is a fundamental control on petrophysics. However, facies scale heterogeneities are not resolvable using current seismic methods, and well data provide little or no data on 3D geometries beyond the well bore. Studies of modern sedimentary events can give some indication of the link between depositional processes and facies distribution (e.g., Kenyon et al., 1995); however preserved depositional architecture is also strongly controlled by changes in accommodation through time (Jervey, 1988). Laboratory-based experiments (e.g., Kneller & Buckee, 2000) and process-based modelling (e.g. Aigner et al., 1989; Peakall et al., 2000) further illustrate the link between depositional mechanism and facies architecture. However, such models are typically on a scale that is far smaller than the typical field and are more applicable to upscaling studies (Nordhal et al., 2005; Ringrose et al., 2005). Outcrop studies have long been employed as a mechanism of studying analogues and understanding petroleum fields (Collinson, 1970; Glennie, 1970; Breed & Grow, 1979). Once the type of depositional system and the accommodation history of a hydrocarbon field are derived from subsurface data, appropriate outcrop analogue(s) can then be identified (e.g. Alexander, 1993). Suitable analogues are those that are geologically comparable to the system that is being studied and also have excellent 3D outcrop exposure over an area that is large enough to capture the scale of heterogeneity required (Clark & Pickering, 1996). Outcrop analogue studies are thus a key way of improving understanding of reservoir facies architecture, geometry, and facies distributions. Outcrop analogue studies have been undertaken both qualitatively and more recently quantitatively. Traditional quantitative studies (e.g., Dreyer et al., 1993; Chapin et al., 1994; Bryant & Flint, 1993; Clark & Pickering, 1996; Reynolds, 1999) have been focused on the collection of outcrop data to populate inter-well reservoir model areas by stochastic, object-based methods (Floris & Peersmann, 2002). However, it can be difficult to extract usable data from traditional outcrop studies, especially when it needs to be integrated with petroleum engineering databases or to be visualized in 3D. Furthermore, outcrops which represent a topographic cut through solid geology are 2D and while rare examples show multiple sections through the solid geology with different orientations, geological expertise is still required to fully understand and interpret the 3D nature of the bodies. Such work may also need geostatistical data manipulation to overcome outcrop orientation and size issues (Geehan & Underwood, 1993; Vissa & Chessa, 2000) but ideally the data should be reconstructed in 3D. Accurate 3D reconstruction is the only way that parameters such as channel sinuosity, connectivity, and continuity of target sandbodies in 3D may be defined. Such parameters are a key control on hydrocarbon production, including sweep efficiency (Pringle et al., 2004a; Larue & Friedmann, 2005). Software for representing geology in 3D is routinely used to model subsurface reservoirs. This paper will show how recent digital data capture technique advances aids the interpreting reservoir geologist by obtaining accurate and quantitative outcrop analogue datasets to aid and perhaps modify his reservoir model.

3D high-resolution digital models of outcrop analogue study sites to constrain reservoir model uncertainty: an example from Alport Castles, Derbyshire, UK

Abstract: Advances in data capture and computer technology have made possible the collection of 3D high-resolution surface and subsurface digital geological data from outcrop analogues. This paper describes research to obtain the 3D distribution and internal sedimentary architecture of turbidite channels and associated sediments at a study site in the Peak District National Park, Derbyshire, UK. The 1D, 2D and 3D digital datasets included Total Station survey, terrestrial photogrammetry and remote sensing, sedimentary logs and a Ground Penetrating Radar (GPR) dataset. A grid of 2D GPR profiles was acquired behind a cliff outcrop; electromagnetic reflection events correlated with cliff face sedimentary horizons logged by Vertical Radar Profiling. All data were combined into a Digital Solid Model (DSM) dataset of the site within reservoir modelling software. The DSM was analysed to extract 3D architectural geometries for petroleum reservoir models. A deterministic base model was created using all information, along with a suite of heterogeneous turbidite reservoir models, using 1D, 2D or 3D information. The model suite shows significant variation from the deterministic model. Models built from 2D information underestimated connectivity and the continuity of geobodies, but overestimated channel sinuosity. Advantages of using 3D digital outcrop analogue data for 3D reservoir models are detailed.

Time-lapse geophysical investigations over a simulated urban clandestine grave.

Abstract: A simulated clandestine shallow grave was created within a heterogeneous, made-ground, urban environment where a clothed, plastic resin, human skeleton, animal products, and physiological saline were placed in anatomically correct positions and re-covered to ground level. A series of repeat (time-lapse), near-surface geophysical surveys were undertaken: (1) prior to burial (to act as control), (2) 1 month, and (3) 3 months post-burial. A range of different geophysical techniques was employed including: bulk ground resistivity and conductivity, fluxgate gradiometry and high-frequency ground penetrating radar (GPR), soil magnetic susceptibility, electrical resistivity tomography (ERT), and self potential (SP). Bulk ground resistivity and SP proved optimal for initial grave location whilst ERT profiles and GPR horizontal "time-slices" showed the best spatial resolutions. Research suggests that in complex urban made-ground environments, initial resistivity surveys be collected before GPR and ERT follow-up surveys are collected over the identified geophysical anomalies.

Terrestrial laser scanning in geology: data acquisition, processing and accuracy considerations

Abstract: Terrestrial laser scanning, or lidar, is a recent innovation in spatial information data acquisition, which allows geological outcrops to be digitally captured with unprecedented resolution and accuracy. With point precisions and spacing of the order of a few centimetres, an enhanced quantitative element can now be added to geological fieldwork and analysis, opening up new lines of investigation at a variety of scales in all areas of field-based geology. Integration with metric imagery allows 3D photorealistic models to be created for interpretation, visualization and education. However, gaining meaningful results from lidar scans requires more than simply acquiring raw point data. Surveys require planning and, typically, a large amount of post-processing time. The contribution of this paper is to provide a more detailed insight into the technology, data collection and utilization techniques than is currently available. The paper focuses on the workflow for using lidar data, from the choice of field area and survey planning, to acquiring and processing data and, finally, extracting geologically useful data. Because manufacturer specifications for point precision are often optimistic when applied to real-world outcrops, the error sources associated with lidar data, and the implications of them propagating through the processing chain, are also discussed.

GPR in sediments: advice on data collection, basic processing and interpretation, a good practice guide

Abstract: Within sedimentological studies, ground penetrating radar (GPR) is being used with increasing frequency because it yields images of the shallow subsurface that cannot be achieved by any other non-destructive method. The purpose of this paper is to provide an introduction to the collection, processing and interpretation of GPR data so that future sedimentary studies can be improved. With GPR equipment now being lightweight, robust and portable, proper data collection and survey design methods need to be followed in order to acquire high resolution, subsurface digital data. Various factors are discussed including: Reflection profiling, velocity soundings, test surveys, topography, logistics, data quality and extreme environments. Basic data processing and visualization are then reviewed, followed by a discussion on GPR interpretation strategies including a background to radar stratigraphy. For the sedimentary geologist or geomorphologist, GPR offers unique data of the shallow subsurface including stratigraphy, geometry, architecture and structure.